@InProceedings{AuraHernandez-Sabate2005,
author="Aura Hernandez-Sabate
and Debora Gil
and Petia Radeva",
title="On the usefulness of supervised learning for vessel border detection in IntraVascular Imaging",
booktitle="Proceeding of the 2005 conference on Artificial Intelligence Research and Development",
year="2005",
publisher="IOS Press",
address="Amsterdam, The Netherlands",
pages="67--74",
optkeywords="classification",
optkeywords="vessel border modelling",
optkeywords="IVUS",
abstract="IntraVascular UltraSound (IVUS) imaging is a useful tool in diagnosis of cardiac diseases since sequences completely show the morphology of coronary vessels. Vessel borders detection, especially the external adventitia layer, plays a central role in morphological measures and, thus, their segmentation feeds development of medical imaging techniques. Deterministic approaches fail to yield optimal results due to the large amount of IVUS artifacts and vessel borders descriptors. We propose using classification techniques to learn the set of descriptors and parameters that best detect vessel borders. Statistical hypothesis test on the error between automated detections and manually traced borders by 4 experts show that our detections keep within inter-observer variability.",
optnote="IAM;MILAB",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1549), last updated on Fri, 08 Jul 2016 11:11:23 +0200",
file=":http://refbase.cvc.uab.es/files/HGR2005c.pdf:PDF"
}